Overview

Dataset statistics

Number of variables18
Number of observations35064
Missing cells7271
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

station has constant value ""Constant
CO is highly overall correlated with NO2 and 3 other fieldsHigh correlation
DEWP is highly overall correlated with PRES and 1 other fieldsHigh correlation
NO2 is highly overall correlated with CO and 4 other fieldsHigh correlation
No is highly overall correlated with yearHigh correlation
O3 is highly overall correlated with NO2 and 1 other fieldsHigh correlation
PM10 is highly overall correlated with CO and 2 other fieldsHigh correlation
PM2.5 is highly overall correlated with CO and 2 other fieldsHigh correlation
PRES is highly overall correlated with DEWP and 1 other fieldsHigh correlation
SO2 is highly overall correlated with COHigh correlation
TEMP is highly overall correlated with DEWP and 2 other fieldsHigh correlation
WSPM is highly overall correlated with NO2High correlation
year is highly overall correlated with NoHigh correlation
PM2.5 has 925 (2.6%) missing valuesMissing
PM10 has 718 (2.0%) missing valuesMissing
SO2 has 935 (2.7%) missing valuesMissing
NO2 has 1023 (2.9%) missing valuesMissing
CO has 1776 (5.1%) missing valuesMissing
O3 has 1719 (4.9%) missing valuesMissing
RAIN is highly skewed (γ1 = 34.71933563)Skewed
No is uniformly distributedUniform
No has unique valuesUnique
hour has 1461 (4.2%) zerosZeros
RAIN has 33664 (96.0%) zerosZeros
WSPM has 1353 (3.9%) zerosZeros

Reproduction

Analysis started2024-03-08 05:06:41.854227
Analysis finished2024-03-08 05:07:19.485693
Duration37.63 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct35064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:19.609070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754.15
Q18766.75
median17532.5
Q326298.25
95-th percentile33310.85
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.249
Coefficient of variation (CV)0.57734204
Kurtosis-1.2
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum6.1475958 × 108
Variance1.0245993 × 108
MonotonicityStrictly increasing
2024-03-08T12:07:19.811338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
23379 1
 
< 0.1%
23373 1
 
< 0.1%
23374 1
 
< 0.1%
23375 1
 
< 0.1%
23376 1
 
< 0.1%
23377 1
 
< 0.1%
23378 1
 
< 0.1%
23380 1
 
< 0.1%
23422 1
 
< 0.1%
Other values (35054) 35054
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
35064 1
< 0.1%
35063 1
< 0.1%
35062 1
< 0.1%
35061 1
< 0.1%
35060 1
< 0.1%
35059 1
< 0.1%
35058 1
< 0.1%
35057 1
< 0.1%
35056 1
< 0.1%
35055 1
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
2016
8784 
2014
8760 
2015
8760 
2013
7344 
2017
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Length

2024-03-08T12:07:19.988034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:07:20.158053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:20.364206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4487524
Coefficient of variation (CV)0.52871219
Kurtosis-1.2080577
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.0092942217
Sum228720
Variance11.893893
MonotonicityNot monotonic
2024-03-08T12:07:20.581730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
1 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5592
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2712
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729637
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:20.758890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8002175
Coefficient of variation (CV)0.55946729
Kurtosis-1.1940295
Mean15.729637
Median Absolute Deviation (MAD)8
Skewness0.0067598056
Sum551544
Variance77.443829
MonotonicityNot monotonic
2024-03-08T12:07:20.957709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1152
 
3.3%
2 1152
 
3.3%
28 1152
 
3.3%
27 1152
 
3.3%
26 1152
 
3.3%
25 1152
 
3.3%
24 1152
 
3.3%
23 1152
 
3.3%
22 1152
 
3.3%
21 1152
 
3.3%
Other values (21) 23544
67.1%
ValueCountFrequency (%)
1 1152
3.3%
2 1152
3.3%
3 1152
3.3%
4 1152
3.3%
5 1152
3.3%
6 1152
3.3%
7 1152
3.3%
8 1152
3.3%
9 1152
3.3%
10 1152
3.3%
ValueCountFrequency (%)
31 672
1.9%
30 1056
3.0%
29 1080
3.1%
28 1152
3.3%
27 1152
3.3%
26 1152
3.3%
25 1152
3.3%
24 1152
3.3%
23 1152
3.3%
22 1152
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:21.130883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-03-08T12:07:21.299586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1461
 
4.2%
1 1461
 
4.2%
22 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct545
Distinct (%)1.6%
Missing925
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean82.773611
Minimum3
Maximum898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:21.494158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q122
median58
Q3114
95-th percentile248
Maximum898
Range895
Interquartile range (IQR)92

Descriptive statistics

Standard deviation82.135694
Coefficient of variation (CV)0.99229323
Kurtosis5.5741713
Mean82.773611
Median Absolute Deviation (MAD)41
Skewness1.9660679
Sum2825808.3
Variance6746.2722
MonotonicityNot monotonic
2024-03-08T12:07:21.701260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 600
 
1.7%
10 590
 
1.7%
12 576
 
1.6%
11 570
 
1.6%
8 563
 
1.6%
13 563
 
1.6%
14 505
 
1.4%
3 454
 
1.3%
7 451
 
1.3%
15 445
 
1.3%
Other values (535) 28822
82.2%
(Missing) 925
 
2.6%
ValueCountFrequency (%)
3 454
1.3%
4 163
 
0.5%
5 276
0.8%
6 360
1.0%
7 451
1.3%
8 563
1.6%
8.6 1
 
< 0.1%
9 600
1.7%
10 590
1.7%
11 570
1.6%
ValueCountFrequency (%)
898 1
< 0.1%
713 2
< 0.1%
697 1
< 0.1%
682 1
< 0.1%
665 2
< 0.1%
657 1
< 0.1%
651 1
< 0.1%
646 1
< 0.1%
644 1
< 0.1%
641 1
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct633
Distinct (%)1.8%
Missing718
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean110.06039
Minimum2
Maximum984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:21.893972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q138
median87
Q3155
95-th percentile291
Maximum984
Range982
Interquartile range (IQR)117

Descriptive statistics

Standard deviation95.223005
Coefficient of variation (CV)0.86518868
Kurtosis5.3836985
Mean110.06039
Median Absolute Deviation (MAD)55
Skewness1.7795686
Sum3780134.2
Variance9067.4206
MonotonicityNot monotonic
2024-03-08T12:07:22.114360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 345
 
1.0%
18 322
 
0.9%
22 313
 
0.9%
14 299
 
0.9%
15 296
 
0.8%
23 296
 
0.8%
20 285
 
0.8%
13 283
 
0.8%
24 281
 
0.8%
17 281
 
0.8%
Other values (623) 31345
89.4%
(Missing) 718
 
2.0%
ValueCountFrequency (%)
2 4
 
< 0.1%
3 27
 
0.1%
4 12
 
< 0.1%
5 177
0.5%
6 345
1.0%
6.4 1
 
< 0.1%
6.6 1
 
< 0.1%
7 178
0.5%
8 211
0.6%
9 232
0.7%
ValueCountFrequency (%)
984 1
< 0.1%
948 1
< 0.1%
944 1
< 0.1%
884 1
< 0.1%
874 1
< 0.1%
873 1
< 0.1%
862 1
< 0.1%
858 1
< 0.1%
845 1
< 0.1%
844 2
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct330
Distinct (%)1.0%
Missing935
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean17.375901
Minimum0.2856
Maximum341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:22.327910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2856
5-th percentile2
Q13
median9
Q321
95-th percentile65
Maximum341
Range340.7144
Interquartile range (IQR)18

Descriptive statistics

Standard deviation22.823017
Coefficient of variation (CV)1.3134868
Kurtosis10.787375
Mean17.375901
Median Absolute Deviation (MAD)7
Skewness2.8017026
Sum593022.14
Variance520.8901
MonotonicityNot monotonic
2024-03-08T12:07:22.827549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6706
19.1%
3 2275
 
6.5%
4 1901
 
5.4%
5 1680
 
4.8%
6 1493
 
4.3%
7 1356
 
3.9%
8 1222
 
3.5%
9 1050
 
3.0%
10 968
 
2.8%
11 877
 
2.5%
Other values (320) 14601
41.6%
(Missing) 935
 
2.7%
ValueCountFrequency (%)
0.2856 2
 
< 0.1%
0.5712 5
 
< 0.1%
0.8568 4
 
< 0.1%
1 34
 
0.1%
1.1424 5
 
< 0.1%
1.428 7
 
< 0.1%
1.7136 5
 
< 0.1%
1.9992 12
 
< 0.1%
2 6706
19.1%
2.2848 12
 
< 0.1%
ValueCountFrequency (%)
341 1
< 0.1%
229 1
< 0.1%
197 1
< 0.1%
196 1
< 0.1%
192 2
< 0.1%
190 1
< 0.1%
189 1
< 0.1%
186 2
< 0.1%
185 2
< 0.1%
182 2
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct465
Distinct (%)1.4%
Missing1023
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean59.305833
Minimum2
Maximum290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:23.020925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q130
median53
Q382
95-th percentile128
Maximum290
Range288
Interquartile range (IQR)52

Descriptive statistics

Standard deviation37.1162
Coefficient of variation (CV)0.625844
Kurtosis1.0362608
Mean59.305833
Median Absolute Deviation (MAD)25
Skewness0.92758217
Sum2018829.9
Variance1377.6123
MonotonicityNot monotonic
2024-03-08T12:07:23.209528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 408
 
1.2%
42 394
 
1.1%
44 393
 
1.1%
28 392
 
1.1%
36 391
 
1.1%
15 387
 
1.1%
34 386
 
1.1%
25 385
 
1.1%
29 382
 
1.1%
32 379
 
1.1%
Other values (455) 30144
86.0%
(Missing) 1023
 
2.9%
ValueCountFrequency (%)
2 85
 
0.2%
3 10
 
< 0.1%
4 30
 
0.1%
5 82
 
0.2%
6 141
0.4%
7 191
0.5%
8 237
0.7%
8.6226 1
 
< 0.1%
9 287
0.8%
10 333
0.9%
ValueCountFrequency (%)
290 1
 
< 0.1%
285 1
 
< 0.1%
280 1
 
< 0.1%
277 2
< 0.1%
269 1
 
< 0.1%
263 1
 
< 0.1%
253 3
< 0.1%
243 2
< 0.1%
240 1
 
< 0.1%
238 1
 
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct112
Distinct (%)0.3%
Missing1776
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean1262.9451
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:23.427280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile300
Q1500
median900
Q31500
95-th percentile3700
Maximum10000
Range9900
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation1221.4362
Coefficient of variation (CV)0.96713324
Kurtosis8.8118943
Mean1262.9451
Median Absolute Deviation (MAD)500
Skewness2.5642139
Sum42040918
Variance1491906.5
MonotonicityNot monotonic
2024-03-08T12:07:23.632844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 2877
 
8.2%
500 2728
 
7.8%
300 2578
 
7.4%
600 2561
 
7.3%
700 2349
 
6.7%
800 1932
 
5.5%
900 1755
 
5.0%
1000 1638
 
4.7%
200 1292
 
3.7%
1100 1281
 
3.7%
Other values (102) 12297
35.1%
(Missing) 1776
 
5.1%
ValueCountFrequency (%)
100 220
 
0.6%
200 1292
3.7%
300 2578
7.4%
400 2877
8.2%
500 2728
7.8%
600 2561
7.3%
700 2349
6.7%
800 1932
5.5%
900 1755
5.0%
1000 1638
4.7%
ValueCountFrequency (%)
10000 5
< 0.1%
9900 6
< 0.1%
9700 4
< 0.1%
9600 4
< 0.1%
9500 3
 
< 0.1%
9400 3
 
< 0.1%
9300 1
 
< 0.1%
9200 2
 
< 0.1%
9100 6
< 0.1%
9000 8
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct831
Distinct (%)2.5%
Missing1719
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean56.353358
Minimum0.2142
Maximum423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:23.826128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q18
median42
Q382
95-th percentile181
Maximum423
Range422.7858
Interquartile range (IQR)74

Descriptive statistics

Standard deviation57.916327
Coefficient of variation (CV)1.0277351
Kurtosis2.1773172
Mean56.353358
Median Absolute Deviation (MAD)36
Skewness1.4446028
Sum1879102.7
Variance3354.3009
MonotonicityNot monotonic
2024-03-08T12:07:24.019379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4224
 
12.0%
1 1520
 
4.3%
3 582
 
1.7%
4 536
 
1.5%
5 449
 
1.3%
6 421
 
1.2%
8 391
 
1.1%
10 359
 
1.0%
7 357
 
1.0%
9 350
 
1.0%
Other values (821) 24156
68.9%
(Missing) 1719
 
4.9%
ValueCountFrequency (%)
0.2142 7
 
< 0.1%
0.4284 5
 
< 0.1%
0.6426 2
 
< 0.1%
0.8568 6
 
< 0.1%
1 1520
 
4.3%
1.071 4
 
< 0.1%
1.2852 7
 
< 0.1%
1.4994 2
 
< 0.1%
1.7136 3
 
< 0.1%
2 4224
12.0%
ValueCountFrequency (%)
423 1
< 0.1%
406 1
< 0.1%
397 1
< 0.1%
380 1
< 0.1%
365 2
< 0.1%
358.9992 1
< 0.1%
352.7874 1
< 0.1%
351 1
< 0.1%
350 1
< 0.1%
342 1
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct967
Distinct (%)2.8%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13.584607
Minimum-16.8
Maximum40.5
Zeros196
Zeros (%)0.6%
Negative5308
Negative (%)15.1%
Memory size274.1 KiB
2024-03-08T12:07:24.213301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-16.8
5-th percentile-4
Q13.1
median14.5
Q323.3
95-th percentile30.6
Maximum40.5
Range57.3
Interquartile range (IQR)20.2

Descriptive statistics

Standard deviation11.399097
Coefficient of variation (CV)0.83911861
Kurtosis-1.1573086
Mean13.584607
Median Absolute Deviation (MAD)9.8
Skewness-0.094157845
Sum476058.98
Variance129.93941
MonotonicityNot monotonic
2024-03-08T12:07:24.413501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 249
 
0.7%
1 240
 
0.7%
2 203
 
0.6%
0 196
 
0.6%
-2 186
 
0.5%
-1 186
 
0.5%
24.1 154
 
0.4%
21.7 150
 
0.4%
-4 143
 
0.4%
22.5 139
 
0.4%
Other values (957) 33198
94.7%
ValueCountFrequency (%)
-16.8 2
< 0.1%
-16.3 1
 
< 0.1%
-16.2 1
 
< 0.1%
-16.1 1
 
< 0.1%
-16 1
 
< 0.1%
-15.9 3
< 0.1%
-15.8 2
< 0.1%
-15.6 1
 
< 0.1%
-15.4 1
 
< 0.1%
-15.3 2
< 0.1%
ValueCountFrequency (%)
40.5 1
< 0.1%
40.3 1
< 0.1%
40.1 1
< 0.1%
39.2 1
< 0.1%
38.8 1
< 0.1%
38.4 1
< 0.1%
38.3 1
< 0.1%
38.1 2
< 0.1%
38 1
< 0.1%
37.9 1
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct600
Distinct (%)1.7%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1011.8469
Minimum985.9
Maximum1042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:24.631375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum985.9
5-th percentile996.1
Q11003.3
median1011.4
Q31020.1
95-th percentile1028.8
Maximum1042
Range56.1
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation10.404047
Coefficient of variation (CV)0.010282234
Kurtosis-0.88797034
Mean1011.8469
Median Absolute Deviation (MAD)8.5
Skewness0.11150001
Sum35459163
Variance108.24419
MonotonicityNot monotonic
2024-03-08T12:07:24.836553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1023 256
 
0.7%
1019 252
 
0.7%
1024 249
 
0.7%
1025 248
 
0.7%
1020 239
 
0.7%
1017 238
 
0.7%
1021 237
 
0.7%
1022 237
 
0.7%
1018 233
 
0.7%
1026 224
 
0.6%
Other values (590) 32631
93.1%
ValueCountFrequency (%)
985.9 1
 
< 0.1%
986.3 1
 
< 0.1%
987.2 1
 
< 0.1%
987.5 1
 
< 0.1%
987.7 2
< 0.1%
987.8 3
< 0.1%
987.9 1
 
< 0.1%
988 4
< 0.1%
988.1 4
< 0.1%
988.2 2
< 0.1%
ValueCountFrequency (%)
1042 1
 
< 0.1%
1041.8 1
 
< 0.1%
1041.6 1
 
< 0.1%
1041.4 1
 
< 0.1%
1041.2 2
< 0.1%
1041.1 2
< 0.1%
1041 2
< 0.1%
1040.9 1
 
< 0.1%
1040.8 3
< 0.1%
1040.7 1
 
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct604
Distinct (%)1.7%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.1230624
Minimum-35.3
Maximum28.5
Zeros66
Zeros (%)0.2%
Negative14885
Negative (%)42.5%
Memory size274.1 KiB
2024-03-08T12:07:25.083428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-35.3
5-th percentile-19.4
Q1-8.1
median3.8
Q315.6
95-th percentile22.4
Maximum28.5
Range63.8
Interquartile range (IQR)23.7

Descriptive statistics

Standard deviation13.688896
Coefficient of variation (CV)4.3831644
Kurtosis-1.1013944
Mean3.1230624
Median Absolute Deviation (MAD)11.9
Skewness-0.21320853
Sum109444.6
Variance187.38587
MonotonicityNot monotonic
2024-03-08T12:07:25.281880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.6 143
 
0.4%
17 133
 
0.4%
17.2 129
 
0.4%
18.8 128
 
0.4%
17.8 126
 
0.4%
17.9 125
 
0.4%
18.3 124
 
0.4%
16.8 124
 
0.4%
17.1 124
 
0.4%
18.2 121
 
0.3%
Other values (594) 33767
96.3%
ValueCountFrequency (%)
-35.3 1
< 0.1%
-35.1 1
< 0.1%
-35 1
< 0.1%
-34.8 1
< 0.1%
-34.5 1
< 0.1%
-34.3 2
< 0.1%
-34.2 1
< 0.1%
-34.1 1
< 0.1%
-33.8 1
< 0.1%
-33.7 1
< 0.1%
ValueCountFrequency (%)
28.5 1
 
< 0.1%
27.8 5
< 0.1%
27.7 1
 
< 0.1%
27.6 1
 
< 0.1%
27.5 3
< 0.1%
27.4 1
 
< 0.1%
27.3 4
< 0.1%
27.2 4
< 0.1%
27.1 2
 
< 0.1%
27 6
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct127
Distinct (%)0.4%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.067420957
Minimum0
Maximum72.5
Zeros33664
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:25.478415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72.5
Range72.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.91005591
Coefficient of variation (CV)13.498116
Kurtosis1822.1806
Mean0.067420957
Median Absolute Deviation (MAD)0
Skewness34.719336
Sum2362.7
Variance0.82820177
MonotonicityNot monotonic
2024-03-08T12:07:25.736408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33664
96.0%
0.1 314
 
0.9%
0.2 161
 
0.5%
0.3 107
 
0.3%
0.5 73
 
0.2%
0.4 72
 
0.2%
0.6 67
 
0.2%
0.7 49
 
0.1%
0.9 48
 
0.1%
0.8 39
 
0.1%
Other values (117) 450
 
1.3%
ValueCountFrequency (%)
0 33664
96.0%
0.1 314
 
0.9%
0.2 161
 
0.5%
0.3 107
 
0.3%
0.4 72
 
0.2%
0.5 73
 
0.2%
0.6 67
 
0.2%
0.7 49
 
0.1%
0.8 39
 
0.1%
0.9 48
 
0.1%
ValueCountFrequency (%)
72.5 1
< 0.1%
46.4 1
< 0.1%
40.7 1
< 0.1%
36.6 1
< 0.1%
33.7 1
< 0.1%
33.1 1
< 0.1%
29.3 1
< 0.1%
26.8 1
< 0.1%
24.1 1
< 0.1%
23.7 1
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing81
Missing (%)0.2%
Memory size274.1 KiB
NE
5140 
ENE
3950 
SW
3359 
E
2608 
NNE
2445 
Other values (11)
17481 

Length

Max length3
Median length2
Mean length2.256839
Min length1

Characters and Unicode

Total characters78951
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNNW
2nd rowN
3rd rowNNW
4th rowNW
5th rowN

Common Values

ValueCountFrequency (%)
NE 5140
14.7%
ENE 3950
11.3%
SW 3359
9.6%
E 2608
 
7.4%
NNE 2445
 
7.0%
WSW 2212
 
6.3%
SSW 2098
 
6.0%
N 2066
 
5.9%
NW 1860
 
5.3%
ESE 1717
 
4.9%
Other values (6) 7528
21.5%

Length

2024-03-08T12:07:25.977726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne 5140
14.7%
ene 3950
11.3%
sw 3359
9.6%
e 2608
 
7.5%
nne 2445
 
7.0%
wsw 2212
 
6.3%
ssw 2098
 
6.0%
n 2066
 
5.9%
nw 1860
 
5.3%
ese 1717
 
4.9%
Other values (6) 7528
21.5%

Most occurring characters

ValueCountFrequency (%)
E 23890
30.3%
N 22185
28.1%
W 16703
21.2%
S 16173
20.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 78951
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 23890
30.3%
N 22185
28.1%
W 16703
21.2%
S 16173
20.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 78951
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 23890
30.3%
N 22185
28.1%
W 16703
21.2%
S 16173
20.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 23890
30.3%
N 22185
28.1%
W 16703
21.2%
S 16173
20.5%

WSPM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)0.3%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.7084964
Minimum0
Maximum11.2
Zeros1353
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:07:26.178761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.9
median1.4
Q32.2
95-th percentile4.1
Maximum11.2
Range11.2
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2040711
Coefficient of variation (CV)0.70475481
Kurtosis2.4952557
Mean1.7084964
Median Absolute Deviation (MAD)0.6
Skewness1.365037
Sum59882.8
Variance1.4497871
MonotonicityNot monotonic
2024-03-08T12:07:26.408321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 1891
 
5.4%
1.1 1842
 
5.3%
1 1747
 
5.0%
1.3 1640
 
4.7%
0.9 1604
 
4.6%
1.4 1581
 
4.5%
0.8 1443
 
4.1%
1.5 1442
 
4.1%
0.7 1366
 
3.9%
0 1353
 
3.9%
Other values (81) 19141
54.6%
ValueCountFrequency (%)
0 1353
3.9%
0.1 397
 
1.1%
0.2 430
 
1.2%
0.3 191
 
0.5%
0.4 602
 
1.7%
0.5 883
2.5%
0.6 1123
3.2%
0.7 1366
3.9%
0.8 1443
4.1%
0.9 1604
4.6%
ValueCountFrequency (%)
11.2 1
 
< 0.1%
9.2 1
 
< 0.1%
9.1 1
 
< 0.1%
8.9 1
 
< 0.1%
8.8 1
 
< 0.1%
8.6 1
 
< 0.1%
8.5 1
 
< 0.1%
8.4 3
< 0.1%
8.3 2
< 0.1%
8.2 2
< 0.1%

station
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Aotizhongxin
35064 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters420768
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAotizhongxin
2nd rowAotizhongxin
3rd rowAotizhongxin
4th rowAotizhongxin
5th rowAotizhongxin

Common Values

ValueCountFrequency (%)
Aotizhongxin 35064
100.0%

Length

2024-03-08T12:07:26.606801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:07:26.735101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
aotizhongxin 35064
100.0%

Most occurring characters

ValueCountFrequency (%)
o 70128
16.7%
i 70128
16.7%
n 70128
16.7%
A 35064
8.3%
t 35064
8.3%
z 35064
8.3%
h 35064
8.3%
g 35064
8.3%
x 35064
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 385704
91.7%
Uppercase Letter 35064
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 70128
18.2%
i 70128
18.2%
n 70128
18.2%
t 35064
9.1%
z 35064
9.1%
h 35064
9.1%
g 35064
9.1%
x 35064
9.1%
Uppercase Letter
ValueCountFrequency (%)
A 35064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 420768
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 70128
16.7%
i 70128
16.7%
n 70128
16.7%
A 35064
8.3%
t 35064
8.3%
z 35064
8.3%
h 35064
8.3%
g 35064
8.3%
x 35064
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 420768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 70128
16.7%
i 70128
16.7%
n 70128
16.7%
A 35064
8.3%
t 35064
8.3%
z 35064
8.3%
h 35064
8.3%
g 35064
8.3%
x 35064
8.3%

Interactions

2024-03-08T12:07:15.984490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:43.676335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:46.080954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:48.368177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:50.626725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:53.096548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:55.235192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:57.361908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:59.922992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:02.176868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:04.427512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:06.572836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:09.114192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:11.314612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:13.620031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:16.138058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:43.832900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:46.235082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:48.507746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:50.767876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:53.233692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:55.376142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:57.520575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:00.096241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:02.319692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:04.570980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:06.731200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:09.261379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:11.479441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:13.812232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:16.504103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:43.980105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:46.381862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:48.676961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:51.191559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:53.366235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:55.499965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:57.671271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:00.239056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:02.487004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:04.705221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:06.874837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:09.408051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:11.635332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:13.975914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:16.658406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:44.125292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:46.544271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:48.830905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:51.330196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:53.504585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:55.631242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:57.826153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:00.376461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:02.615768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:04.855019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:07.005584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:09.555521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:11.787968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:14.127136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:16.790416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:44.306069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:46.680848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:48.965432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:51.454618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:53.637639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:55.775168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:57.987793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:00.517646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:02.768145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:04.981624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:07.144685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:09.693084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:11.937091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:14.281368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:16.936300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:44.471298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:46.837811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:49.101328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:51.600722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:53.781624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:55.923048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:58.121606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:00.645302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:02.901210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:05.121067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:07.299912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:09.837553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:12.071760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:14.448973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:17.084158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:44.620451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:46.986052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:49.261927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:51.737290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:53.914759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:56.067193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:58.263059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:00.804210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:03.051340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:05.258332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:07.471037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:09.978893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:12.226281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:14.597383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:17.233263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:44.782170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:47.149141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:49.395270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:51.897154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:54.070636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:56.201849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:58.411277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:00.960816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:03.221127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:05.385736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:07.629564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:10.129682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:12.389426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:14.750810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:17.374148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:44.934952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:47.296514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:49.545457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:52.046577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:54.216264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:56.336474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:58.565577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:01.123026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:03.385990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:05.524063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:07.771389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:10.293199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:12.542801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:14.899580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:17.513784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:45.086619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:47.458969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:49.683454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:52.182994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:54.359504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:56.485062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:58.731636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:01.258869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:03.518756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:05.645277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:07.903312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:10.421309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:12.680953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:15.039149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:17.643953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:45.232964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:47.604794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:49.836907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:52.306211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:54.496914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:56.619700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:58.867378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:01.396215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:03.663289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:05.772035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:08.301358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:10.569369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:12.837974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:15.188011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:17.806190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:45.384044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:47.745450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:49.985212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:52.445186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:54.628553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:56.761602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:59.025167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:01.533434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:03.803386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:05.921262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:08.443093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:10.707163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:12.996108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:15.340331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:17.959989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:45.553832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:47.883798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:50.139706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:52.591026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:54.753918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:56.896581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:59.170571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:01.698171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:03.965015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:06.079194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:08.591191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:10.836351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:13.130813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:15.498402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:18.122215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:45.696839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:48.047822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:50.294240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:52.761761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:54.923362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:57.027250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:59.318779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:01.858284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:04.126731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:06.234123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:08.783504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:10.979293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:13.275864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:15.656629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:18.296857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:45.878889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:48.237964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:50.468577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:52.955468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:55.085324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:57.188722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:06:59.792012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:02.033024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:04.288789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:06.401084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:08.993713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:11.143399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:13.450758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:15.826102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T12:07:26.847157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CODEWPNO2NoO3PM10PM2.5PRESRAINSO2TEMPWSPMdayhourmonthwdyear
CO1.0000.0220.746-0.007-0.4640.7230.8260.160-0.0000.559-0.285-0.3710.001-0.0340.0380.1060.076
DEWP0.0221.0000.081-0.1330.2240.1320.248-0.7820.174-0.3770.821-0.2550.023-0.0130.2600.1160.158
NO20.7460.0811.000-0.124-0.6500.6540.6870.083-0.0610.448-0.235-0.5430.022-0.0750.1130.1300.115
No-0.007-0.133-0.1241.0000.081-0.119-0.0760.2280.001-0.285-0.1030.1110.0180.0010.0440.1170.862
O3-0.4640.224-0.6500.0811.000-0.243-0.264-0.3930.001-0.2560.5640.461-0.0130.294-0.1530.1590.077
PM100.7230.1320.654-0.119-0.2431.0000.895-0.085-0.0850.496-0.044-0.2520.0330.032-0.0360.0820.072
PM2.50.8260.2480.687-0.076-0.2640.8951.000-0.102-0.0250.475-0.017-0.3350.020-0.0120.0100.0870.058
PRES0.160-0.7820.0830.228-0.393-0.085-0.1021.000-0.0850.280-0.8310.0490.013-0.0360.0030.0760.168
RAIN-0.0000.174-0.0610.0010.001-0.085-0.025-0.0851.000-0.1620.041-0.021-0.005-0.0040.0430.0040.009
SO20.559-0.3770.448-0.285-0.2560.4960.4750.280-0.1621.000-0.400-0.0750.0070.017-0.2410.0520.089
TEMP-0.2850.821-0.235-0.1030.564-0.044-0.017-0.8310.041-0.4001.0000.0970.0170.1450.1230.1040.147
WSPM-0.371-0.255-0.5430.1110.461-0.252-0.3350.049-0.021-0.0750.0971.000-0.0080.171-0.1780.1590.077
day0.0010.0230.0220.018-0.0130.0330.0200.013-0.0050.0070.017-0.0081.0000.0000.0100.0270.000
hour-0.034-0.013-0.0750.0010.2940.032-0.012-0.036-0.0040.0170.1450.1710.0001.0000.0000.1310.000
month0.0380.2600.1130.044-0.153-0.0360.0100.0030.043-0.2410.123-0.1780.0100.0001.0000.0870.249
wd0.1060.1160.1300.1170.1590.0820.0870.0760.0040.0520.1040.1590.0270.1310.0871.0000.131
year0.0760.1580.1150.8620.0770.0720.0580.1680.0090.0890.1470.0770.0000.0000.2490.1311.000

Missing values

2024-03-08T12:07:18.491434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T12:07:18.907062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-08T12:07:19.306419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133104.04.04.07.0300.077.0-0.71023.0-18.80.0NNW4.4Aotizhongxin
1220133118.08.04.07.0300.077.0-1.11023.2-18.20.0N4.7Aotizhongxin
2320133127.07.05.010.0300.073.0-1.11023.5-18.20.0NNW5.6Aotizhongxin
3420133136.06.011.011.0300.072.0-1.41024.5-19.40.0NW3.1Aotizhongxin
4520133143.03.012.012.0300.072.0-2.01025.2-19.50.0N2.0Aotizhongxin
5620133155.05.018.018.0400.066.0-2.21025.6-19.60.0N3.7Aotizhongxin
6720133163.03.018.032.0500.050.0-2.61026.5-19.10.0NNE2.5Aotizhongxin
7820133173.06.019.041.0500.043.0-1.61027.4-19.10.0NNW3.8Aotizhongxin
8920133183.06.016.043.0500.045.00.11028.3-19.20.0NNW4.1Aotizhongxin
91020133193.08.012.028.0400.059.01.21028.5-19.30.0N2.6Aotizhongxin
NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
35054350552017228145.05.04.08.0100.0117.014.61013.3-15.60.0N3.6Aotizhongxin
35055350562017228156.019.04.08.0100.0122.015.41013.0-15.00.0NNW3.3Aotizhongxin
350563505720172281612.018.05.09.0200.0122.014.91012.6-15.40.0NW2.1Aotizhongxin
350573505820172281712.023.06.013.0200.0120.014.21012.5-14.90.0NW3.1Aotizhongxin
350583505920172281813.029.05.022.0300.0109.013.41013.0-15.50.0WNW1.4Aotizhongxin
350593506020172281912.029.05.035.0400.095.012.51013.5-16.20.0NW2.4Aotizhongxin
350603506120172282013.037.07.045.0500.081.011.61013.6-15.10.0WNW0.9Aotizhongxin
350613506220172282116.037.010.066.0700.058.010.81014.2-13.30.0NW1.1Aotizhongxin
350623506320172282221.044.012.087.0700.035.010.51014.4-12.90.0NNW1.2Aotizhongxin
350633506420172282319.031.010.079.0600.042.08.61014.1-15.90.0NNE1.3Aotizhongxin